---
title: Genomic Feature Analysis of CMR
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    theme: cosmo
---

```{r setup, include=FALSE}
library(flexdashboard)
library(rtracklayer)
library(plotly)
library(ggplot2)
library(seqinr)
library(GenomicFeatures)
library(knitr)
library(kableExtra)
options(scipen = 20)
knitr::opts_chunk$set(echo = TRUE,
                      warning = FALSE,
                      message = FALSE)
```


```{r echo = FALSE, warning = FALSE, message = FALSE}
# gene GC content
getGCContent <- function(inputSeq){
  res <- unlist(lapply(inputSeq, function(x) GC(s2c(as.character(x)))))
  res
}

CMRGene <- read.table(file = CMRGeneDir, sep = "\t", header = F, quote = '',
                      stringsAsFactors = F)
CMRGene <- CMRGene$V1
GTF <- import(con = GTFDir)
GTF <- subset(GTF, GTF$gene_biotype == "protein_coding")
txdb <- makeTxDbFromGFF(file = GTFDir)
GTFGene <- data.frame(subset(GTF, GTF$type == "gene"))
rownames(GTFGene) <- GTFGene$gene_id
GTFdfGeneBed <- data.frame(GTFGene$seqnames,
                           GTFGene$start-1,
                           GTFGene$end,
                           GTFGene$gene_id,
                           ".",
                           GTFGene$strand)
write.table(GTFdfGeneBed, file = paste0(getwd(), "/gene.bed"),
            sep = '\t', quote = F, row.names = F, col.names = F)
cmd <- paste0('bedtools getfasta -s -name -bed ',  paste0(getwd(), "/gene.bed "),
              " -fi ", genome, " -fo ", paste0(getwd(), "/gene.fasta"))
system(command = cmd)
geneSeq <- read.fasta(paste0(getwd(), "/gene.fasta"), as.string = T)
names(geneSeq) <- gsub(pattern = "\\(|\\+|\\-|\\)", replacement = "", x = names(geneSeq))
geneSeq <- lapply(geneSeq, as.character)

seqNames <- na.omit(as.numeric(as.character(unique(seqnames(GTF)@values))))

GTFdf <- data.frame(GTF)

resFreq <- NULL
resGCContent <- NULL
for(i in 1:length(seqNames)){
  curGTF <- subset(GTFdf, GTFdf$seqnames == i)
  curGeneGTF <- subset(curGTF, curGTF$type == "gene")
  curGeneGTF <- curGeneGTF[order(curGeneGTF$start), ]
  rownames(curGeneGTF) <- curGeneGTF$gene_id
  orderGene <- rownames(curGeneGTF)

  curLength <- length(unique(curGTF$gene_id))
  curStart <- seq(1, curLength - seqLength + stepSize, stepSize)
  curEnd <- c(curStart[1:(length(curStart)-1)] + seqLength - 1, curLength)
  curMat <- cbind(curStart, curEnd)
  CMRFreq <- apply(curMat, 1,
                   function(x) length(intersect(orderGene[x[1]:x[2]],
                                                CMRGene))/seqLength)
  curGCContent <- unlist(apply(curMat, 1,
                               function(x) mean(getGCContent(geneSeq[orderGene[x[1]:x[2]]]))))
  resFreq <- c(resFreq, CMRFreq)
  resGCContent <- c(resGCContent, curGCContent)
}
```


### Introduction
- The function provides the correlation analysis of CMR genes with multiple gene features including **Mean gene length**, **Mean exon number**, **Mean GC content** and **Mean distance to adjacent gene**. To be specific, we split the maize genome in a sliding window of 100 adjacent genes with step size of 10, and calculate the frequency of CMR genes in each window (100 adjacent genes).


Row {data-height=250}
---------------------------------------------------------------

### Correlation of CMR genes with mean gene length

```{r echo = FALSE, warning=FALSE, message=FALSE}
df <- data.frame(freq = resFreq*100, 
                 GCContent = resGCContent*100)
p1 <- ggplot(df, aes(x = freq, y = GCContent)) + 
      geom_point(colour = "grey") + 
      geom_smooth(method = "lm", se = FALSE, colour = "red") + 
      labs(x = "Frequency of CMR genes (%)", y = "Mean GC content")
ggplotly(p1)
```



### Statistics of linear regression of CMR genes with mean GC content

```{r echo = FALSE, warning = FALSE, message = FALSE}
group <- gl(n = 2, k = nrow(df), labels = c("freq", "GCContent"))
values <- c(df$freq, df$GCContent)
lmRes <- lm(values ~ group)
res.summary <- summary(lmRes)
res.table <- data.frame(Stats = c("r.squared",
                                  "adj.r.squared",
                                  "fstatistic",
                                  "sigma",
                                  "df"),
                        values = c(res.summary$r.squared,
                                   res.summary$adj.r.squared,
                                   paste(res.summary$fstatistic, collapse = " "),
                                   res.summary$sigma,
                                   paste(res.summary$df, collapse = " ")))
knitr::kable(x = res.table, align = 'c') %>%  
  kableExtra::kable_styling(position = 'center', full_width = FALSE, 
                            stripe_color = 'black', latex_options = "bordered")
```


### Anova analysis of CMR genes with mean GC content

```{r echo = FALSE, warning = FALSE, message = FALSE}
knitr::kable(x = t(anova(lmRes)), align = "c") %>%
  kableExtra::kable_styling(position = "center", full_width = FALSE,
                            stripe_color = "black", latex_options = "bordered")
```





Row {data-height=250}
---------------------------------------------------------------

```{r echo = FALSE, warning = FALSE, message = FALSE}
nonCMRGene <- setdiff(unique(GTFdf$gene_id), CMRGene)
nonCMRGene <- sample(nonCMRGene, size = ratio*length(CMRGene))
```

### GC content difference between CMR genes and non-CMR genes
```{r echo = FALSE, warning=FALSE, message=FALSE}
cmr.GCContent <- as.numeric(getGCContent(geneSeq[CMRGene]))
noncmr.GCContent <- as.numeric(getGCContent(geneSeq[nonCMRGene]))

cmrStat <- boxplot.stats(cmr.GCContent)$stats
tmpCMR <- cmr.GCContent[which(cmr.GCContent <= cmrStat[5])]

noncmrStat <- boxplot.stats(noncmr.GCContent)$stats
tmpNonCMR <- noncmr.GCContent[which(noncmr.GCContent <= noncmrStat[5])]

df <- data.frame(type = factor(rep(c("CMRGene", "nonCMRGene"),
                                   c(length(tmpCMR), length(tmpNonCMR)))),
                 value = c(tmpCMR*100, tmpNonCMR*100))

p <- ggplot(df, aes(x=type, y=value, fill=type)) + 
  geom_violin(trim = FALSE) +
  theme(axis.title.x = element_blank(),
        #axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.title = element_blank(),
        legend.position = "none")
ggplotly(p)
```

### The statistics of GC content difference between CMR genes and non-CMR genes
```{r echo = FALSE, warning=FALSE, message=FALSE}
res <- data.frame(Stats = c("Min", "First quantile", "Median", "Third quantile", "Max"),
                  CMR = cmrStat, nonCMR = noncmrStat)
knitr::kable(x = res, align = 'c') %>%  
  kableExtra::kable_styling(position = 'center', full_width = FALSE, 
                            stripe_color = 'black', latex_options = "bordered")
```




### GC content density difference between CMR genes and non-CMR genes
```{r echo = FALSE, warning = FALSE, message = FALSE}
p <- ggplot(df, aes(x=value, fill=type)) + 
  geom_density(alpha = 0.5) +
  theme(axis.title.x = element_blank(),
        #axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.title = element_blank(),
        legend.position = "none")
ggplotly(p)
```